Multiple endemic states in age-structured $SIR$ epidemic models

doi:10.3934/mbe.2012.9.577

Mathematical Biosciences and Engineering, 2012, 9(3): 577-599. doi: 10.3934/mbe.2012.9.577. Primary: 92D30; Secondary: 45G15, 65P30.

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Multiple endemic states in age-structured $SIR$ epidemic models

1. Dept. Mathematics, Università di Trento, Via Sommarive 14, 38123 Povo (TN)
2. Department of Mathematics and Computer Science, University of Udine, via delle Scienze 206, I33100 Udine

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$SIR$ age-structured models are very often used as a basic model of epidemic spread. Yet, their behaviour, under generic assumptions on contact rates between different age classes, is not completely known, and, in the most detailed analysis so far, Inaba (1990) was able to prove uniqueness of the endemic equilibrium only under a rather restrictive condition.
Here, we show an example in the form of a $3 \times 3$ contact matrix in which multiple non-trivial steady states exist. This instance of non-uniqueness of positive equilibria differs from most existing ones for epidemic models, since it arises not from a backward transcritical bifurcation at the disease free equilibrium, but through two saddle-node bifurcations of the positive equilibrium. The dynamical behaviour of the model is analysed numerically around the range where multiple endemic equilibria exist; many other features are shown to occur, from coexistence of multiple attractive periodic solutions, some with extremely long period, to quasi-periodic and chaotic attractors.
It is also shown that, if the contact rates are in the form of a $2 \times 2$ WAIFW matrix, uniqueness of non-trivial steady states always holds, so that 3 is the minimum dimension of the contact matrix to allow for multiple endemic equilibria.

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Keywords: periodic and chaotic solutions; Age-structured epidemic model; multiple endemic equilibria; fixed point index.; numerical bifurcation analysis

Citation: Andrea Franceschetti, Andrea Pugliese, Dimitri Breda. Multiple endemic states in age-structured $SIR$ epidemic models. Mathematical Biosciences and Engineering, 2012, 9(3): 577-599. doi: 10.3934/mbe.2012.9.577

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